NBA: Basketball-Reference.com: Playoffs classification:: Playoffs2
bdanalytics
Date: (Tue) Jun 16, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_train.csv
New: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_test.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Low.cor.X.glm: [1, 0]; W 100.00; AST 25.45; STL 23.41; FT 20.34; X3PA 17.24; X2PA 16.24 W.glm: [1, 0]; W 100.00
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_train.csv"
glb_newdt_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_test.csv"
glb_out_pfx <- "Playoffs2_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newent_dataset <- TRUE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Playoffs"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Playoffs.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))## [1] Y Y N N N
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
as.numeric(var) - 1
#as.numeric(var)
#gsub("\\.", " ", levels(var)[as.numeric(var)])
#c(" <=50K", " >50K")[as.numeric(var)]
#c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))## [1] 1 1 0 0 0if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# SeasonEnd: is the year the season ended.
# Team: Team ID
# Playoffs: 1 if team made it to the playoffs that year else 0
# W: # of regular season wins.
# PTS: # of points scored during the regular season.
# oppPTS: # of opponent points scored during the regular season.
# FG: # of successful field goals, including two and three pointers.
# FGA: # of FG attempts
# X2P: # of 2 point field goals made
# X2PA: # of 2 point field goals attmpted
# X3P, X3PA: # of 3 point field goals
# FT, FTA: # of free throws
# ORB, DRB: # of offensive and defensive rebounds.
# AST: # of assists.
# STL: # of steals.
# BLK: # of blocks.
# TOV: # of turnovers.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- NULL # or c("<var1>")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
glb_transform_lst <- NULL;
# glb_transform_lst[["<var>"]] <- list(
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , sfx=".my.fctr")
# mapfn(glb_allobs_df$<var>)
# glb_transform_lst[["<var1>"]] <- glb_transform_lst[["<var2>"]]
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_transform_vars <- names(glb_transform_lst)
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- NULL # or c("<txt_var1>", "<txt_var2>")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- NULL # or c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("Team.fctr")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.377 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/NBA_train.csv..."
## [1] "dimensions of data in ./data/NBA_train.csv: 835 rows x 20 cols"
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 1980 Atlanta Hawks 1 50 8573 8334 3261 7027 3248
## 2 1980 Boston Celtics 1 61 9303 8664 3617 7387 3455
## 3 1980 Chicago Bulls 0 30 8813 9035 3362 6943 3292
## 4 1980 Cleveland Cavaliers 0 37 9360 9332 3811 8041 3775
## 5 1980 Denver Nuggets 0 30 8878 9240 3462 7470 3379
## 6 1980 Detroit Pistons 0 16 8933 9609 3643 7596 3586
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 6952 13 75 2038 2645 1369 2406 1913 782 539 1495
## 2 6965 162 422 1907 2449 1227 2457 2198 809 308 1539
## 3 6668 70 275 2019 2592 1115 2465 2152 704 392 1684
## 4 7854 36 187 1702 2205 1307 2381 2108 764 342 1370
## 5 7215 83 255 1871 2539 1311 2524 2079 746 404 1533
## 6 7377 57 219 1590 2149 1226 2415 1950 783 562 1742
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 29 1981 Detroit Pistons 0 21 8174 8692 3236 6986 3223
## 127 1985 Los Angeles Lakers 1 62 9696 9093 3952 7254 3862
## 426 1997 Chicago Bulls 1 69 8458 7572 3277 6923 2754
## 607 2004 Los Angeles Clippers 0 28 7771 8147 2817 6579 2488
## 611 2004 Milwaukee Bucks 1 41 8039 7952 2970 6650 2569
## 825 2011 New York Knicks 1 42 8734 8670 3140 6867 2375
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 29 6902 13 84 1689 2330 1201 2111 1819 884 492 1759
## 127 6959 90 295 1702 2232 1063 2550 2575 695 481 1537
## 426 5520 523 1403 1381 1848 1235 2461 2142 716 332 1109
## 607 5555 329 1024 1808 2302 1149 2416 1653 594 376 1344
## 611 5505 401 1145 1698 2192 960 2502 1872 554 383 1110
## 825 4786 765 2081 1689 2087 847 2470 1757 625 475 1123
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA
## 830 2011 Portland Trail Blazers 1 48 7896 7771 2951 6599
## 831 2011 Sacramento Kings 0 24 8151 8589 3134 6979
## 832 2011 San Antonio Spurs 1 61 8502 8034 3148 6628
## 833 2011 Toronto Raptors 0 22 8124 8639 3144 6755
## 834 2011 Utah Jazz 0 39 8153 8303 3064 6590
## 835 2011 Washington Wizards 0 23 7977 8584 3048 6888
## X2P X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 830 2433 5096 518 1503 1476 1835 996 2230 1736 660 358 1070
## 831 2706 5702 428 1277 1455 1981 1071 2526 1675 608 391 1324
## 832 2463 4901 685 1727 1521 1984 829 2603 1836 602 372 1101
## 833 2799 5664 345 1091 1491 1976 963 2343 1795 581 350 1206
## 834 2629 5334 435 1256 1590 2061 898 2338 1921 629 484 1175
## 835 2656 5706 392 1182 1489 1999 1013 2374 1592 665 502 1258
## 'data.frame': 835 obs. of 20 variables:
## $ SeasonEnd: int 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
## $ Team : chr "Atlanta Hawks" "Boston Celtics" "Chicago Bulls" "Cleveland Cavaliers" ...
## $ Playoffs : int 1 1 0 0 0 0 0 1 0 1 ...
## $ W : int 50 61 30 37 30 16 24 41 37 47 ...
## $ PTS : int 8573 9303 8813 9360 8878 8933 8493 9084 9119 8860 ...
## $ oppPTS : int 8334 8664 9035 9332 9240 9609 8853 9070 9176 8603 ...
## $ FG : int 3261 3617 3362 3811 3462 3643 3527 3599 3639 3582 ...
## $ FGA : int 7027 7387 6943 8041 7470 7596 7318 7496 7689 7489 ...
## $ X2P : int 3248 3455 3292 3775 3379 3586 3500 3495 3551 3557 ...
## $ X2PA : int 6952 6965 6668 7854 7215 7377 7197 7117 7375 7375 ...
## $ X3P : int 13 162 70 36 83 57 27 104 88 25 ...
## $ X3PA : int 75 422 275 187 255 219 121 379 314 114 ...
## $ FT : int 2038 1907 2019 1702 1871 1590 1412 1782 1753 1671 ...
## $ FTA : int 2645 2449 2592 2205 2539 2149 1914 2326 2333 2250 ...
## $ ORB : int 1369 1227 1115 1307 1311 1226 1155 1394 1398 1187 ...
## $ DRB : int 2406 2457 2465 2381 2524 2415 2437 2217 2326 2429 ...
## $ AST : int 1913 2198 2152 2108 2079 1950 2028 2149 2148 2123 ...
## $ STL : int 782 809 704 764 746 783 779 782 900 863 ...
## $ BLK : int 539 308 392 342 404 562 339 373 530 356 ...
## $ TOV : int 1495 1539 1684 1370 1533 1742 1492 1565 1517 1439 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newent_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/NBA_test.csv..."
## [1] "dimensions of data in ./data/NBA_test.csv: 28 rows x 20 cols"
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 2013 Atlanta Hawks 1 44 8032 7999 3084 6644 2378
## 2 2013 Brooklyn Nets 1 49 7944 7798 2942 6544 2314
## 3 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## 4 2013 Chicago Bulls 1 45 7641 7615 2926 6698 2480
## 5 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 6 2013 Dallas Mavericks 0 41 8293 8342 3182 6892 2576
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 4743 706 1901 1158 1619 758 2593 2007 664 369 1219
## 2 4784 628 1760 1432 1958 1047 2460 1668 599 391 1206
## 3 5250 469 1399 1546 2060 917 2389 1587 591 479 1153
## 4 5433 446 1265 1343 1738 1026 2514 1886 588 417 1171
## 5 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149
## 6 5264 606 1628 1323 1669 767 2670 1906 648 454 1144
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 2013 Atlanta Hawks 1 44 8032 7999 3084 6644 2378
## 5 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 10 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 11 2013 Los Angeles Clippers 1 56 8289 7760 3160 6608 2533
## 13 2013 Memphis Grizzlies 1 56 7659 7319 2964 6679 2582
## 25 2013 San Antonio Spurs 1 58 8448 7923 3210 6675 2547
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 4743 706 1901 1158 1619 758 2593 2007 664 369 1219
## 5 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149
## 10 4413 867 2369 1573 2087 909 2652 1902 679 359 1348
## 11 4856 627 1752 1342 1888 938 2475 1958 784 461 1197
## 13 5572 382 1107 1349 1746 1059 2445 1715 703 436 1144
## 25 4911 663 1764 1365 1725 666 2721 2058 695 446 1206
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 23 2013 Portland Trail Blazers 0 33 7995 8255 3009 6715 2336
## 24 2013 Sacramento Kings 0 28 8219 8619 3086 6904 2476
## 25 2013 San Antonio Spurs 1 58 8448 7923 3210 6675 2547
## 26 2013 Toronto Raptors 0 34 7971 8092 2979 6685 2408
## 27 2013 Utah Jazz 0 43 8038 8045 3046 6710 2539
## 28 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 23 4811 673 1904 1304 1680 874 2474 1784 538 353 1203
## 24 5223 610 1681 1437 1869 943 2385 1708 671 342 1199
## 25 4911 663 1764 1365 1725 666 2721 2058 695 446 1206
## 26 5020 571 1665 1442 1831 871 2426 1765 595 392 1124
## 27 5325 507 1385 1439 1883 989 2457 1859 690 515 1210
## 28 5198 545 1495 1279 1746 887 2652 1775 598 376 1238
## 'data.frame': 28 obs. of 20 variables:
## $ SeasonEnd: int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ Team : chr "Atlanta Hawks" "Brooklyn Nets" "Charlotte Bobcats" "Chicago Bulls" ...
## $ Playoffs : int 1 1 0 1 0 0 1 0 1 1 ...
## $ W : int 44 49 21 45 24 41 57 29 47 45 ...
## $ PTS : int 8032 7944 7661 7641 7913 8293 8704 7778 8296 8688 ...
## $ oppPTS : int 7999 7798 8418 7615 8297 8342 8287 8105 8223 8403 ...
## $ FG : int 3084 2942 2823 2926 2993 3182 3339 2979 3130 3124 ...
## $ FGA : int 6644 6544 6649 6698 6901 6892 6983 6638 6840 6782 ...
## $ X2P : int 2378 2314 2354 2480 2446 2576 2818 2466 2472 2257 ...
## $ X2PA : int 4743 4784 5250 5433 5320 5264 5465 5198 5208 4413 ...
## $ X3P : int 706 628 469 446 547 606 521 513 658 867 ...
## $ X3PA : int 1901 1760 1399 1265 1581 1628 1518 1440 1632 2369 ...
## $ FT : int 1158 1432 1546 1343 1380 1323 1505 1307 1378 1573 ...
## $ FTA : int 1619 1958 2060 1738 1826 1669 2148 1870 1744 2087 ...
## $ ORB : int 758 1047 917 1026 1004 767 1092 991 885 909 ...
## $ DRB : int 2593 2460 2389 2514 2359 2670 2601 2463 2801 2652 ...
## $ AST : int 2007 1668 1587 1886 1694 1906 2002 1742 1845 1902 ...
## $ STL : int 664 599 591 588 647 648 762 574 567 679 ...
## $ BLK : int 369 391 479 417 334 454 533 400 346 359 ...
## $ TOV : int 1219 1206 1153 1171 1149 1144 1253 1241 1236 1348 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(glb_trnobs_df)
glb_newobs_df$.rownames <- rownames(glb_newobs_df)
glb_id_var <- ".rownames"
}## Warning: using .rownames as identifiers for observationsif (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
# Combine trnent & newent into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.377 10.802 0.425
## 2 inspect.data 2 0 10.803 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## Loading required package: reshape2
## Playoffs.0 Playoffs.1
## Test 14 14
## Train 355 480
## Playoffs.0 Playoffs.1
## Test 0.5000000 0.5000000
## Train 0.4251497 0.5748503
## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## Playoffs Playoffs.fctr .n
## 1 1 Y 494
## 2 0 N 369
## Playoffs.fctr.N Playoffs.fctr.Y
## Test 14 14
## Train 355 480
## Playoffs.fctr.N Playoffs.fctr.Y
## Test 0.5000000 0.5000000
## Train 0.4251497 0.5748503# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, id_vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- df[, id_vars, FALSE]
dates_df[, rsp_var] <- df[, rsp_var, FALSE]
#dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df <- cbind(dates_df, data.frame(.date=strptime(df[, var],
glb_date_fmts[[var]], tz=glb_date_tzs[[var]])))
# print(dates_df[is.na(dates_df$.date), c("ID", "Arrest.fctr", ".date")])
# print(glb_allobs_df[is.na(dates_df$.date), c("ID", "Arrest.fctr", "Date")])
# print(head(glb_allobs_df[grepl("4/7/02 .:..", glb_allobs_df$Date), c("ID", "Arrest.fctr", "Date")]))
# print(head(strptime(glb_allobs_df[grepl("4/7/02 .:..", glb_allobs_df$Date), "Date"], "%m/%e/%y %H:%M"))
# Wrong data during EST->EDT transition
# tmp <- strptime("4/7/02 2:00","%m/%e/%y %H:%M:%S"); print(tmp); print(is.na(tmp))
# dates_df[dates_df$ID == 2068197, .date] <- tmp
# grep("(.*?) 2:(.*)", glb_allobs_df[is.na(dates_df$.date), "Date"], value=TRUE)
# dates_df[is.na(dates_df$.date), ".date"] <-
# data.frame(.date=strptime(gsub("(.*?) 2:(.*)", "\\1 3:\\2",
# glb_allobs_df[is.na(dates_df$.date), "Date"]), "%m/%e/%y %H:%M"))$.date
if (sum(is.na(dates_df$.date)) > 0) {
stop("NA POSIX dates for ", var)
print(df[is.na(dates_df$.date), c(id_vars, rsp_var, var)])
}
.date <- dates_df$.date
dates_df[, paste0(var, ".POSIX")] <- .date
dates_df[, paste0(var, ".year")] <- as.numeric(format(.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(.date, "%d")), 5) # by month week
dates_df[, paste0(var, ".juliandate")] <- as.numeric(format(.date, "%j"))
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(.date, "%w"))
# Get US Federal Holidays for relevant years
require(XML)
doc.html = htmlTreeParse('http://about.usps.com/news/events-calendar/2012-federal-holidays.htm', useInternal = TRUE)
# # Extract all the paragraphs (HTML tag is p, starting at
# # the root of the document). Unlist flattens the list to
# # create a character vector.
# doc.text = unlist(xpathApply(doc.html, '//p', xmlValue))
# # Replace all \n by spaces
# doc.text = gsub('\\n', ' ', doc.text)
# # Join all the elements of the character vector into a single
# # character string, separated by spaces
# doc.text = paste(doc.text, collapse = ' ')
# parse the tree by tables
txt <- unlist(strsplit(xpathSApply(doc.html, "//*/table", xmlValue), "\n"))
# do some clean up with regular expressions
txt <- grep("day, ", txt, value=TRUE)
txt <- trimws(gsub("(.*?)day, (.*)", "\\2", txt))
# txt <- gsub("\t","",txt)
# txt <- sub("^[[:space:]]*(.*?)[[:space:]]*$", "\\1", txt, perl=TRUE)
# txt <- txt[!(txt %in% c("", "|"))]
hldays <- strptime(paste(txt, ", 2012", sep=""), "%B %e, %Y")
dates_df[, paste0(var, ".hlday")] <-
ifelse(format(.date, "%Y-%m-%d") %in% hldays, 1, 0)
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%H")))) <= 1)
vals else cut(vals, 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%M")))) <= 1)
vals else cut(vals, 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%S")))) <= 1)
vals else cut(vals, 4) # by quarter-minutes
dates_df[, paste0(var, ".day.minutes")] <-
60 * dates_df[, paste0(var, ".hour")] +
dates_df[, paste0(var, ".minute")]
if ((unq_vals_n <- length(unique(dates_df[, paste0(var, ".day.minutes")]))) > 1) {
max_degree <- min(unq_vals_n, 5)
dates_df[, paste0(var, ".day.minutes.poly.", 1:max_degree)] <-
as.matrix(poly(dates_df[, paste0(var, ".day.minutes")], max_degree))
} else max_degree <- 0
# print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.day.minutes",
# xcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name=".rownames",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.1", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.day.minutes",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var))
#
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name=c("PubDate.day.minutes", "PubDate.day.minutes.poly.4"),
# colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=subset(dates_df, Popular.fctr=="Y"),
# xcol_name=paste0(var, ".juliandate"),
# ycol_name=paste0(var, ".day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_box(df=dates_df, ycol_names=paste0(var, ".hour"),
# xcol_name=rsp_var))
# print(gp <- myplot_bar(df=dates_df, ycol_names=paste0(var, ".hour.fctr"),
# xcol_name=rsp_var,
# colorcol_name=paste0(var, ".hour.fctr")))
keep_feats <- paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr"), sep="")
if (max_degree > 0)
keep_feats <- union(keep_feats, paste(var,
paste0(".day.minutes.poly.", 1:max_degree), sep=""))
keep_feats <- intersect(keep_feats, names(dates_df))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: SeasonEnd"
## [1] "feat: W"
## [1] "feat: PTS"
## [1] "feat: oppPTS"
## [1] "feat: FG"
## [1] "feat: FGA"
## [1] "feat: X2P"
## [1] "feat: X2PA"
## [1] "feat: X3P"
## [1] "feat: X3PA"
## [1] "feat: FT"
## [1] "feat: FTA"
## [1] "feat: ORB"
## [1] "feat: DRB"
## [1] "feat: AST"
## [1] "feat: STL"
## [1] "feat: BLK"
## [1] "feat: TOV"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
rm(srt_allobs_df, last1, last10, last100, pd)## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object
## 'srt_allobs_df' not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last1'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last10'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last100'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'pd' not
## foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 10.803 20.805 10.002
## 3 scrub.data 2 1 20.805 NA NAStep 2.1: scrub data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# if (!is.null(glb_force_0_to_NA_vars)) {
# for (feat in glb_force_0_to_NA_vars) {
# warning("Forcing ", sum(glb_allobs_df[, feat] == 0),
# " obs with ", feat, " 0s to NAs")
# glb_allobs_df[glb_allobs_df[, feat] == 0, feat] <- NA
# }
# }
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
# sel_obs <- function(Popular=NULL,
# NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
# Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
# Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
# perl=FALSE) {
sel_obs <- function(vars_lst) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 20.805 23.074 2.269
## 4 transform.data 2 2 23.074 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Transformations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_transform_vars) {
new_feat <- paste0(feat, glb_transform_lst[[feat]]$sfx)
print(sprintf("Applying mapping function for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_transform_lst[[feat]]$mapfn(glb_allobs_df[, feat])
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_transform_vars)
}Step 2.2: transform data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 23.074 23.109 0.035
## 5 manage.missing.data 2 3 23.109 NA NA# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
# complete(mice()) changes attributes of factors even though values don't change
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0Step 2.3: manage missing data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 manage.missing.data 2 3 23.109 23.202 0.093
## 6 extract.features 3 0 23.202 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 23.21 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 23.21 23.22
## 2 extract.features_factorize.str.vars 2 0 23.22 NA
## elapsed
## 1 0.01
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## Team .rownames .src
## "Team" ".rownames" ".src"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <- factor(glb_allobs_df[, var],
as.factor(unique(glb_allobs_df[, var])))
# glb_trnobs_df[, paste0(var, ".fctr")] <- factor(glb_trnobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
# glb_newobs_df[, paste0(var, ".fctr")] <- factor(glb_newobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: Team: # of unique values: 38if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(re_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(re_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
#tmp_freq_df <- chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE)
#subset(chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE), grepl("New [[:upper:]]", pattern))
#chk_pattern_freq("\\bnew (\\W)+")
chk_subfn <- function(pos_ix) {
re_str <- gsubfn_args_lst[["re_str"]][[pos_ix]]
print("re_str:"); print(re_str)
rp_frmla <- gsubfn_args_lst[["rp_frmla"]][[pos_ix]]
print("rp_frmla:"); print(rp_frmla, showEnv=FALSE)
tmp_vctr <- grep(re_str, txt_vctr, value=TRUE, ignore.case=TRUE)[1:5]
print("Before:")
print(tmp_vctr)
print("After:")
print(gsubfn(re_str, rp_frmla, tmp_vctr, ignore.case=TRUE))
}
#chk_subfn(1)
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end], glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#all.equal(sav_txt_lst[["Headline"]][1:2000], glb_txt_lst[["Headline"]][1:2000])
#all.equal(sav_txt_lst[["Headline"]][1:1000], glb_txt_lst[["Headline"]][1:1000])
#all.equal(sav_txt_lst[["Headline"]][1:500], glb_txt_lst[["Headline"]][1:500])
#all.equal(sav_txt_lst[["Headline"]][1:200], glb_txt_lst[["Headline"]][1:200])
#all.equal(sav_txt_lst[["Headline"]][1:100], glb_txt_lst[["Headline"]][1:100])
#chk.equal( 1, 100)
#chk.equal(51, 100)
#chk.equal(81, 100)
#chk.equal(81, 90)
#chk.equal(81, 85)
#chk.equal(86, 90)
#chk.equal(96, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(tmp_vctr <- grep("[[:upper:]]\\.", txt_vctr, value=TRUE, ignore.case=FALSE))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create user-specified pattern vectors
# <txt_var>.P.year.colon
txt_X_df[, paste0(txt_var_pfx, ".P.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.daily.clip.report")] <-
as.integer(0 + mycount_pattern_occ("Daily Clip Report", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.fashion.week")] <-
as.integer(0 + mycount_pattern_occ("Fashion Week", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.first.draft")] <-
as.integer(0 + mycount_pattern_occ("First Draft", glb_allobs_df[, txt_var]))
#sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
if (txt_var %in% c("Snippet", "Abstract")) {
txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
glb_allobs_df[, txt_var]))
}
#sum(mycount_pattern_occ("[0-9]{4}:", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df$Headline, perl=TRUE) > 0)
#sum(mycount_pattern_occ("No Comment(.*):", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Friday Night Music:", glb_allobs_df$Headline) > 0)
if (txt_var %in% c("Headline")) {
txt_X_df[, paste0(txt_var_pfx, ".P.facts.figures")] <-
as.integer(0 + mycount_pattern_occ("Facts & Figures:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.friday.night.music")] <-
as.integer(0 + mycount_pattern_occ("Friday Night Music", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.no.comment.colon")] <-
as.integer(0 + mycount_pattern_occ("No Comment(.*):", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.on.this.day")] <-
as.integer(0 + mycount_pattern_occ("On This Day", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.quandary")] <-
as.integer(0 + mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df[, txt_var], perl=TRUE))
txt_X_df[, paste0(txt_var_pfx, ".P.readers.respond")] <-
as.integer(0 + mycount_pattern_occ("Readers Respond", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.recap.colon")] <-
as.integer(0 + mycount_pattern_occ("Recap:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.s.notebook")] <-
as.integer(0 + mycount_pattern_occ("s Notebook", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.politic")] <-
as.integer(0 + mycount_pattern_occ("Today in Politic", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.smallbusiness")] <-
as.integer(0 + mycount_pattern_occ("Today in Small Business:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.verbatim.colon")] <-
as.integer(0 + mycount_pattern_occ("Verbatim:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.what.we.are")] <-
as.integer(0 + mycount_pattern_occ("What We're", glb_allobs_df[, txt_var]))
}
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_DTM' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_full_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_sprs_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_corpus' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 23.220 23.243
## 3 extract.features_end 3 0 23.244 NA
## elapsed
## 2 0.023
## 3 NAmyplt_chunk(extract.features_chunk_df)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 23.22 23.243
## 1 extract.features_bgn 1 0 23.21 23.220
## elapsed duration
## 2 0.023 0.023
## 1 0.010 0.010
## [1] "Total Elapsed Time: 23.243 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 23.202 24.669 1.467
## 7 cluster.data 4 0 24.669 NA NAStep 4.0: cluster data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 cluster.data 4 0 24.669 24.983 0.314
## 8 select.features 5 0 24.983 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id cor.y exclude.as.feat cor.y.abs
## Playoffs Playoffs 1.000000000 1 1.000000000
## W W 0.798675595 0 0.798675595
## DRB DRB 0.344749300 0 0.344749300
## AST AST 0.314705084 0 0.314705084
## PTS PTS 0.270601328 0 0.270601328
## oppPTS oppPTS -0.232760995 0 0.232760995
## FT FT 0.221799063 0 0.221799063
## FG FG 0.187552799 0 0.187552799
## BLK BLK 0.187056116 0 0.187056116
## FTA FTA 0.179413465 0 0.179413465
## STL STL 0.173822947 0 0.173822947
## Team.fctr Team.fctr -0.172716430 1 0.172716430
## TOV TOV -0.168601605 0 0.168601605
## X2P X2P 0.108033881 0 0.108033881
## SeasonEnd SeasonEnd -0.059127866 0 0.059127866
## ORB ORB -0.041702843 0 0.041702843
## .rnorm .rnorm 0.030300464 0 0.030300464
## X3P X3P 0.028382516 0 0.028382516
## FGA FGA -0.011985459 0 0.011985459
## X2PA X2PA -0.009931886 0 0.009931886
## X3PA X3PA 0.006570622 0 0.006570622# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(FT, FTA)=0.9505"
## [1] "cor(Playoffs.fctr, FT)=0.2218"
## [1] "cor(Playoffs.fctr, FTA)=0.1794"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FTA as highly correlated with FT## [1] "cor(FG, X2P)=0.9429"
## [1] "cor(Playoffs.fctr, FG)=0.1876"
## [1] "cor(Playoffs.fctr, X2P)=0.1080"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified X2P as highly correlated with FG## [1] "cor(FG, PTS)=0.9420"
## [1] "cor(Playoffs.fctr, FG)=0.1876"
## [1] "cor(Playoffs.fctr, PTS)=0.2706"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FG as highly correlated with PTS## [1] "cor(oppPTS, PTS)=0.7891"
## [1] "cor(Playoffs.fctr, oppPTS)=-0.2328"
## [1] "cor(Playoffs.fctr, PTS)=0.2706"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified oppPTS as highly correlated with PTS## [1] "cor(AST, PTS)=0.7599"
## [1] "cor(Playoffs.fctr, AST)=0.3147"
## [1] "cor(Playoffs.fctr, PTS)=0.2706"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified PTS as highly correlated with AST## [1] "cor(SeasonEnd, TOV)=-0.7239"
## [1] "cor(Playoffs.fctr, SeasonEnd)=-0.0591"
## [1] "cor(Playoffs.fctr, TOV)=-0.1686"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified SeasonEnd as highly correlated with TOV## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## 11 Playoffs 1.000000000 1 1.000000000 <NA> 1.352113
## 17 W 0.798675595 0 0.798675595 <NA> 1.000000
## 4 DRB 0.344749300 0 0.344749300 <NA> 1.428571
## 2 AST 0.314705084 0 0.314705084 <NA> 1.000000
## 12 PTS 0.270601328 0 0.270601328 AST 1.000000
## 7 FT 0.221799063 0 0.221799063 <NA> 1.166667
## 5 FG 0.187552799 0 0.187552799 PTS 1.000000
## 3 BLK 0.187056116 0 0.187056116 <NA> 1.125000
## 8 FTA 0.179413465 0 0.179413465 FT 1.000000
## 14 STL 0.173822947 0 0.173822947 <NA> 1.000000
## 18 X2P 0.108033881 0 0.108033881 FG 1.200000
## 1 .rnorm 0.030300464 0 0.030300464 <NA> 1.000000
## 20 X3P 0.028382516 0 0.028382516 <NA> 1.500000
## 21 X3PA 0.006570622 0 0.006570622 <NA> 1.000000
## 19 X2PA -0.009931886 0 0.009931886 <NA> 1.000000
## 6 FGA -0.011985459 0 0.011985459 <NA> 1.000000
## 10 ORB -0.041702843 0 0.041702843 <NA> 1.000000
## 13 SeasonEnd -0.059127866 0 0.059127866 TOV 1.000000
## 16 TOV -0.168601605 0 0.168601605 <NA> 1.166667
## 15 Team.fctr -0.172716430 1 0.172716430 <NA> 1.000000
## 9 oppPTS -0.232760995 0 0.232760995 PTS 1.000000
## percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 11 0.239521 FALSE FALSE FALSE FALSE
## 17 7.065868 FALSE FALSE FALSE FALSE
## 4 49.700599 FALSE FALSE FALSE FALSE
## 2 62.874251 FALSE FALSE FALSE FALSE
## 12 81.197605 FALSE FALSE FALSE FALSE
## 7 57.964072 FALSE FALSE FALSE FALSE
## 5 70.898204 FALSE FALSE FALSE FALSE
## 3 36.526946 FALSE FALSE FALSE FALSE
## 8 63.473054 FALSE FALSE FALSE FALSE
## 14 38.562874 FALSE FALSE FALSE FALSE
## 18 72.934132 FALSE FALSE FALSE FALSE
## 1 100.000000 FALSE FALSE FALSE FALSE
## 20 57.724551 FALSE FALSE FALSE TRUE
## 21 77.844311 FALSE FALSE FALSE TRUE
## 19 85.748503 FALSE FALSE FALSE TRUE
## 6 76.287425 FALSE FALSE FALSE TRUE
## 10 51.736527 FALSE FALSE FALSE FALSE
## 13 3.712575 FALSE FALSE FALSE FALSE
## 16 53.772455 FALSE FALSE FALSE FALSE
## 15 4.431138 FALSE FALSE FALSE FALSE
## 9 83.353293 FALSE FALSE FALSE FALSE#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## [1] id cor.y exclude.as.feat cor.y.abs
## [5] cor.high.X freqRatio percentUnique zeroVar
## [9] nzv myNearZV is.cor.y.abs.low
## <0 rows> (or 0-length row.names)glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
## Playoffs
## 369
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Team .rownames
## 0 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 24.983 25.736 0.754
## 9 partition.data.training 6 0 25.737 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for Playoffs.fctr; setting OOB to Newdata"if (!is.null(glb_max_fitent_obs) && (nrow(glb_fitobs_df) > glb_max_fitent_obs)) {
warning("glb_fitobs_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newent_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 21 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## Playoffs.fctr Playoffs.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 11 Playoffs 1 TRUE 1 <NA>
## Playoffs.fctr Playoffs.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV
## 11 1.352113 0.239521 FALSE FALSE FALSE
## Playoffs.fctr NA NA NA NA NA
## is.cor.y.abs.low interaction.feat rsp_var_raw rsp_var
## 11 FALSE NA TRUE NA
## Playoffs.fctr NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 863 26print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 835 25print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 835 25print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 28 25print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 28 25# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 25.737 26.08 0.343
## 10 fit.models 7 0 26.080 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## Y N
## 0.5748503 0.4251497
## [1] "MFO.val:"
## [1] "Y"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5748503 0.4251497
## 2 0.5748503 0.4251497
## 3 0.5748503 0.4251497
## 4 0.5748503 0.4251497
## 5 0.5748503 0.4251497
## 6 0.5748503 0.4251497
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.MFO.myMFO_classfr.N
## 1 N 355
## 2 Y 480
## Prediction
## Reference N Y
## N 355 0
## Y 480 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.251497e-01 0.000000e+00 3.913345e-01 4.594951e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 5.814170e-106
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5748503 0.4251497
## 2 0.5748503 0.4251497
## 3 0.5748503 0.4251497
## 4 0.5748503 0.4251497
## 5 0.5748503 0.4251497
## 6 0.5748503 0.4251497
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.MFO.myMFO_classfr.N
## 1 N 14
## 2 Y 14
## Prediction
## Reference N Y
## N 14 0
## Y 14 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5000000000 0.0000000000 0.3064709615 0.6935290385 0.5000000000
## AccuracyPValue McnemarPValue
## 0.5747229904 0.0005120045
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.305 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.4251497
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.3913345 0.4594951 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.306471 0.693529 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.7300380
## 3 0.2 0.7300380
## 4 0.3 0.7300380
## 5 0.4 0.7300380
## 6 0.5 0.5422315
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Random.myrandom_classfr.Y
## 1 N 355
## 2 Y 480
## Prediction
## Reference N Y
## N 0 355
## Y 0 480
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.748503e-01 0.000000e+00 5.405049e-01 6.086655e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 5.146549e-01 9.402461e-79
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.6666667
## 3 0.2 0.6666667
## 4 0.3 0.6666667
## 5 0.4 0.6666667
## 6 0.5 0.4666667
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Random.myrandom_classfr.Y
## 1 N 14
## 2 Y 14
## Prediction
## Reference N Y
## N 0 14
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5000000000 0.0000000000 0.3064709615 0.6935290385 0.5000000000
## AccuracyPValue McnemarPValue
## 0.5747229904 0.0005120045
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.229 0.002 0.4623826
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.730038 0.5748503
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5405049 0.6086655 0 0.4285714
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6666667 0.5
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.306471 0.693529 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: W, DRB"## Loading required package: rpart## Fitting cp = 0.811 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.8112676 0 1
##
## Node number 1: 835 observations
## predicted class=Y expected loss=0.4251497 P(node) =1
## class counts: 355 480
## probabilities: 0.425 0.575
##
## n= 835
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 835 355 Y (0.4251497 0.5748503) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 N 355
## 2 Y 480
## Prediction
## Reference N Y
## N 0 355
## Y 0 480
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.748503e-01 0.000000e+00 5.405049e-01 6.086655e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 5.146549e-01 9.402461e-79
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 N 14
## 2 Y 14
## Prediction
## Reference N Y
## N 0 14
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5000000000 0.0000000000 0.3064709615 0.6935290385 0.5000000000
## AccuracyPValue McnemarPValue
## 0.5747229904 0.0005120045
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart W, DRB 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.635 0.019 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.730038 0.5748503
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5405049 0.6086655 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6666667 0.5
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.306471 0.693529 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: W, DRB"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.811267606 0 1.0000000
## 2 0.004929577 1 0.1887324
## 3 0.002816901 5 0.1690141
## 4 0.000000000 9 0.1577465
##
## Variable importance
## W DRB
## 84 16
##
## Node number 1: 835 observations, complexity param=0.8112676
## predicted class=Y expected loss=0.4251497 P(node) =1
## class counts: 355 480
## probabilities: 0.425 0.575
## left son=2 (328 obs) right son=3 (507 obs)
## Primary splits:
## W < 38.5 to the left, improve=285.29670, (0 missing)
## DRB < 2386 to the left, improve= 34.12326, (0 missing)
## Surrogate splits:
## DRB < 2372.5 to the left, agree=0.674, adj=0.171, (0 split)
##
## Node number 2: 328 observations, complexity param=0.002816901
## predicted class=N expected loss=0.06097561 P(node) =0.3928144
## class counts: 308 20
## probabilities: 0.939 0.061
## left son=4 (257 obs) right son=5 (71 obs)
## Primary splits:
## W < 34.5 to the left, improve=6.7188650, (0 missing)
## DRB < 2454.5 to the right, improve=0.4746166, (0 missing)
## Surrogate splits:
## DRB < 2629.5 to the left, agree=0.79, adj=0.028, (0 split)
##
## Node number 3: 507 observations, complexity param=0.004929577
## predicted class=Y expected loss=0.09270217 P(node) =0.6071856
## class counts: 47 460
## probabilities: 0.093 0.907
## left son=6 (116 obs) right son=7 (391 obs)
## Primary splits:
## W < 42.5 to the left, improve=16.59687, (0 missing)
## DRB < 2584 to the left, improve= 1.75519, (0 missing)
##
## Node number 4: 257 observations
## predicted class=N expected loss=0.007782101 P(node) =0.3077844
## class counts: 255 2
## probabilities: 0.992 0.008
##
## Node number 5: 71 observations, complexity param=0.002816901
## predicted class=N expected loss=0.2535211 P(node) =0.08502994
## class counts: 53 18
## probabilities: 0.746 0.254
## left son=10 (20 obs) right son=11 (51 obs)
## Primary splits:
## DRB < 2454.5 to the right, improve=2.306573, (0 missing)
## W < 37.5 to the left, improve=1.728001, (0 missing)
##
## Node number 6: 116 observations, complexity param=0.004929577
## predicted class=Y expected loss=0.3275862 P(node) =0.1389222
## class counts: 38 78
## probabilities: 0.328 0.672
## left son=12 (42 obs) right son=13 (74 obs)
## Primary splits:
## W < 40.5 to the left, improve=2.050681, (0 missing)
## DRB < 2280 to the left, improve=1.624203, (0 missing)
##
## Node number 7: 391 observations
## predicted class=Y expected loss=0.0230179 P(node) =0.4682635
## class counts: 9 382
## probabilities: 0.023 0.977
##
## Node number 10: 20 observations
## predicted class=N expected loss=0.05 P(node) =0.0239521
## class counts: 19 1
## probabilities: 0.950 0.050
##
## Node number 11: 51 observations, complexity param=0.002816901
## predicted class=N expected loss=0.3333333 P(node) =0.06107784
## class counts: 34 17
## probabilities: 0.667 0.333
## left son=22 (40 obs) right son=23 (11 obs)
## Primary splits:
## W < 37.5 to the left, improve=2.575758, (0 missing)
## DRB < 2405 to the left, improve=1.468286, (0 missing)
##
## Node number 12: 42 observations, complexity param=0.004929577
## predicted class=Y expected loss=0.452381 P(node) =0.0502994
## class counts: 19 23
## probabilities: 0.452 0.548
## left son=24 (12 obs) right son=25 (30 obs)
## Primary splits:
## DRB < 2462 to the right, improve=1.5428570, (0 missing)
## W < 39.5 to the left, improve=0.1731602, (0 missing)
##
## Node number 13: 74 observations, complexity param=0.004929577
## predicted class=Y expected loss=0.2567568 P(node) =0.08862275
## class counts: 19 55
## probabilities: 0.257 0.743
## left son=26 (9 obs) right son=27 (65 obs)
## Primary splits:
## DRB < 2280.5 to the left, improve=3.4432430, (0 missing)
## W < 41.5 to the left, improve=0.2432432, (0 missing)
##
## Node number 22: 40 observations
## predicted class=N expected loss=0.25 P(node) =0.04790419
## class counts: 30 10
## probabilities: 0.750 0.250
##
## Node number 23: 11 observations
## predicted class=Y expected loss=0.3636364 P(node) =0.01317365
## class counts: 4 7
## probabilities: 0.364 0.636
##
## Node number 24: 12 observations
## predicted class=N expected loss=0.3333333 P(node) =0.01437126
## class counts: 8 4
## probabilities: 0.667 0.333
##
## Node number 25: 30 observations, complexity param=0.002816901
## predicted class=Y expected loss=0.3666667 P(node) =0.03592814
## class counts: 11 19
## probabilities: 0.367 0.633
## left son=50 (19 obs) right son=51 (11 obs)
## Primary splits:
## DRB < 2391 to the left, improve=2.6414670, (0 missing)
## W < 39.5 to the left, improve=0.9333333, (0 missing)
##
## Node number 26: 9 observations
## predicted class=N expected loss=0.3333333 P(node) =0.01077844
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 27: 65 observations
## predicted class=Y expected loss=0.2 P(node) =0.07784431
## class counts: 13 52
## probabilities: 0.200 0.800
##
## Node number 50: 19 observations
## predicted class=N expected loss=0.4736842 P(node) =0.02275449
## class counts: 10 9
## probabilities: 0.526 0.474
##
## Node number 51: 11 observations
## predicted class=Y expected loss=0.09090909 P(node) =0.01317365
## class counts: 1 10
## probabilities: 0.091 0.909
##
## n= 835
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 835 355 Y (0.425149701 0.574850299)
## 2) W< 38.5 328 20 N (0.939024390 0.060975610)
## 4) W< 34.5 257 2 N (0.992217899 0.007782101) *
## 5) W>=34.5 71 18 N (0.746478873 0.253521127)
## 10) DRB>=2454.5 20 1 N (0.950000000 0.050000000) *
## 11) DRB< 2454.5 51 17 N (0.666666667 0.333333333)
## 22) W< 37.5 40 10 N (0.750000000 0.250000000) *
## 23) W>=37.5 11 4 Y (0.363636364 0.636363636) *
## 3) W>=38.5 507 47 Y (0.092702170 0.907297830)
## 6) W< 42.5 116 38 Y (0.327586207 0.672413793)
## 12) W< 40.5 42 19 Y (0.452380952 0.547619048)
## 24) DRB>=2462 12 4 N (0.666666667 0.333333333) *
## 25) DRB< 2462 30 11 Y (0.366666667 0.633333333)
## 50) DRB< 2391 19 9 N (0.526315789 0.473684211) *
## 51) DRB>=2391 11 1 Y (0.090909091 0.909090909) *
## 13) W>=40.5 74 19 Y (0.256756757 0.743243243)
## 26) DRB< 2280.5 9 3 N (0.666666667 0.333333333) *
## 27) DRB>=2280.5 65 13 Y (0.200000000 0.800000000) *
## 7) W>=42.5 391 9 Y (0.023017903 0.976982097) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9190751
## 3 0.2 0.9190751
## 4 0.3 0.9358717
## 5 0.4 0.9416581
## 6 0.5 0.9415449
## 7 0.6 0.9415449
## 8 0.7 0.9376980
## 9 0.8 0.8888889
## 10 0.9 0.8888889
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 318
## 2 Y 20
## Playoffs.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 37
## 2 460
## Prediction
## Reference N Y
## N 318 37
## Y 20 460
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.317365e-01 8.594670e-01 9.124581e-01 9.478914e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 7.665483e-120 3.406920e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.8965517
## 3 0.2 0.8965517
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.8965517
## 9 0.8 0.9285714
## 10 0.9 0.9285714
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 13
## 2 Y 1
## Playoffs.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 1
## 2 13
## Prediction
## Reference N Y
## N 13 1
## Y 1 13
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 1.000000e+00
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart W, DRB 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.459 0.016 0.9727201
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9416581 0.9317365
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9124581 0.9478914 0.859467 0.9566327
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.9285714 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7649652 0.9912295 0.8571429if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: W, DRB"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00493 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.811267606 0 1.0000000
## 2 0.004929577 1 0.1887324
##
## Variable importance
## W DRB
## 85 15
##
## Node number 1: 835 observations, complexity param=0.8112676
## predicted class=Y expected loss=0.4251497 P(node) =1
## class counts: 355 480
## probabilities: 0.425 0.575
## left son=2 (328 obs) right son=3 (507 obs)
## Primary splits:
## W < 38.5 to the left, improve=285.29670, (0 missing)
## DRB < 2386 to the left, improve= 34.12326, (0 missing)
## Surrogate splits:
## DRB < 2372.5 to the left, agree=0.674, adj=0.171, (0 split)
##
## Node number 2: 328 observations
## predicted class=N expected loss=0.06097561 P(node) =0.3928144
## class counts: 308 20
## probabilities: 0.939 0.061
##
## Node number 3: 507 observations
## predicted class=Y expected loss=0.09270217 P(node) =0.6071856
## class counts: 47 460
## probabilities: 0.093 0.907
##
## n= 835
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 835 355 Y (0.42514970 0.57485030)
## 2) W< 38.5 328 20 N (0.93902439 0.06097561) *
## 3) W>=38.5 507 47 Y (0.09270217 0.90729783) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9321175
## 3 0.2 0.9321175
## 4 0.3 0.9321175
## 5 0.4 0.9321175
## 6 0.5 0.9321175
## 7 0.6 0.9321175
## 8 0.7 0.9321175
## 9 0.8 0.9321175
## 10 0.9 0.9321175
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.rpart.N
## 1 N 308
## 2 Y 20
## Playoffs.fctr.predict.Max.cor.Y.rpart.Y
## 1 47
## 2 460
## Prediction
## Reference N Y
## N 308 47
## Y 20 460
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.197605e-01 8.342002e-01 8.992163e-01 9.372770e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 3.265448e-110 1.491123e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.8965517
## 3 0.2 0.8965517
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.8965517
## 9 0.8 0.8965517
## 10 0.9 0.8965517
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.rpart.N
## 1 N 12
## 2 Y 1
## Playoffs.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 13
## Prediction
## Reference N Y
## N 12 2
## Y 1 13
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.928571e-01 7.857143e-01 7.177356e-01 9.773349e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.372024e-05 1.000000e+00
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart W, DRB 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.004 0.019 0.9129695
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.9321175 0.9053875
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8992163 0.937277 0.8048875 0.8928571
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.8965517 0.8928571
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7177356 0.9773349 0.7857143
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03060035 0.06433844# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: W, DRB"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.88252 -0.08966 0.01378 0.18339 2.92215
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.706e+01 3.417e+00 -4.993 5.93e-07 ***
## W 4.738e-01 4.085e-02 11.598 < 2e-16 ***
## DRB -6.183e-04 1.324e-03 -0.467 0.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.77 on 834 degrees of freedom
## Residual deviance: 308.68 on 832 degrees of freedom
## AIC: 314.68
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9035917
## 3 0.2 0.9208211
## 4 0.3 0.9321357
## 5 0.4 0.9321175
## 6 0.5 0.9318182
## 7 0.6 0.9320794
## 8 0.7 0.9102703
## 9 0.8 0.8856172
## 10 0.9 0.8537736
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.glm.N
## 1 N 300
## 2 Y 13
## Playoffs.fctr.predict.Max.cor.Y.glm.Y
## 1 55
## 2 467
## Prediction
## Reference N Y
## N 300 55
## Y 13 467
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.185629e-01 8.307853e-01 8.978991e-01 9.362084e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 2.733522e-109 6.627244e-07
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.9333333
## 4 0.3 0.9333333
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.9285714
## 9 0.8 0.9285714
## 10 0.9 0.9230769
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Max.cor.Y.glm.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm W, DRB 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.914 0.022 0.9778638
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.9321357 0.9173779
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8978991 0.9362084 0.8299249 0.9897959
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7649652 0.9912295 0.8571429 314.6768
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02848851 0.05891038if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: W, DRB, W:AST, W:PTS, W:FT, W:FG, W:TOV"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.55528 -0.06660 0.00817 0.14413 2.87976
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.026e+01 3.856e+00 -5.254 1.49e-07 ***
## W 4.484e-01 9.067e-02 4.946 7.58e-07 ***
## DRB 2.964e-04 1.429e-03 0.207 0.83563
## `W:AST` 1.087e-04 3.424e-05 3.176 0.00150 **
## `W:PTS` -4.034e-05 3.182e-05 -1.268 0.20490
## `W:FT` 1.051e-04 3.820e-05 2.751 0.00594 **
## `W:FG` 8.670e-06 6.062e-05 0.143 0.88627
## `W:TOV` -1.532e-05 3.867e-05 -0.396 0.69198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.8 on 834 degrees of freedom
## Residual deviance: 281.8 on 827 degrees of freedom
## AIC: 297.8
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9127517
## 3 0.2 0.9303238
## 4 0.3 0.9313433
## 5 0.4 0.9344097
## 6 0.5 0.9396111
## 7 0.6 0.9347368
## 8 0.7 0.9179266
## 9 0.8 0.9026549
## 10 0.9 0.8517200
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 317
## 2 Y 21
## Playoffs.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 38
## 2 459
## Prediction
## Reference N Y
## N 317 38
## Y 21 459
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.293413e-01 8.545361e-01 9.097989e-01 9.457796e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 7.437607e-118 3.724917e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.8965517
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.9285714
## 7 0.6 0.9285714
## 8 0.7 0.9285714
## 9 0.8 0.9230769
## 10 0.9 0.8800000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats max.nTuningRuns
## 1 W, DRB, W:AST, W:PTS, W:FT, W:FG, W:TOV 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.179 0.033 0.9818075
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9396111 0.9221741
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9097989 0.9457796 0.8400543 0.9846939
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7649652 0.9912295 0.8571429 297.8003
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02095379 0.04392501# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: W, DRB, AST, FT, BLK, STL, .rnorm, X3P, X3PA, X2PA, FGA, ORB, TOV"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.67406 -0.06962 0.00809 0.14806 2.97710
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.349e+01 5.439e+00 -4.318 1.57e-05 ***
## W 4.765e-01 4.746e-02 10.039 < 2e-16 ***
## DRB 1.368e-03 1.967e-03 0.696 0.48669
## AST 3.415e-03 1.301e-03 2.625 0.00867 **
## FT 2.207e-03 1.000e-03 2.207 0.02734 *
## BLK 1.270e-03 2.032e-03 0.625 0.53212
## STL 5.769e-03 2.776e-03 2.078 0.03770 *
## .rnorm 2.020e-01 1.544e-01 1.308 0.19080
## X3P -9.499e-03 8.863e-03 -1.072 0.28385
## X3PA 1.773e-03 3.654e-03 0.485 0.62747
## X2PA -1.287e-03 8.678e-04 -1.483 0.13805
## FGA NA NA NA NA
## ORB -1.185e-03 1.947e-03 -0.609 0.54282
## TOV -1.998e-03 1.849e-03 -1.080 0.27992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.77 on 834 degrees of freedom
## Residual deviance: 275.27 on 822 degrees of freedom
## AIC: 301.27
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9127517
## 3 0.2 0.9266862
## 4 0.3 0.9304175
## 5 0.4 0.9338759
## 6 0.5 0.9411765
## 7 0.6 0.9412998
## 8 0.7 0.9202586
## 9 0.8 0.9042316
## 10 0.9 0.8714953
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Low.cor.X.glm.N
## 1 N 330
## 2 Y 31
## Playoffs.fctr.predict.Low.cor.X.glm.Y
## 1 25
## 2 449
## Prediction
## Reference N Y
## N 330 25
## Y 31 449
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329341e-01 8.630947e-01 9.137899e-01 9.489450e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 7.568269e-121 5.040359e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.9333333
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.9285714
## 9 0.8 0.9285714
## 10 0.9 0.8800000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.Low.cor.X.glm.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.Low.cor.X.glm.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 W, DRB, AST, FT, BLK, STL, .rnorm, X3P, X3PA, X2PA, FGA, ORB, TOV
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.008 0.053
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9827054 0.6 0.9412998 0.9233602
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9137899 0.948945 0.8426415 0.9846939
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7649652 0.9912295 0.8571429 301.2685
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02391429 0.04992758rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 26.08 49.399 23.319
## 11 fit.models 7 1 49.40 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 53.058 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 53.058 53.072 0.014
## 2 fit.models_1_glm 2 0 53.073 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.53981 -0.06407 0.00768 0.13929 3.00413
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 76.6077099 94.1498423 0.814 0.41583
## W 0.4874659 0.0686597 7.100 1.25e-12 ***
## DRB 0.0013320 0.0023542 0.566 0.57153
## AST 0.0039410 0.0014844 2.655 0.00793 **
## PTS -0.0105494 0.0094248 -1.119 0.26300
## FT 0.0180695 0.0103434 1.747 0.08064 .
## FG 0.0189566 0.0194098 0.977 0.32874
## BLK 0.0014321 0.0021015 0.681 0.49560
## FTA -0.0040937 0.0027653 -1.480 0.13876
## STL 0.0053928 0.0030949 1.742 0.08142 .
## X2P NA NA NA NA
## .rnorm 0.2251992 0.1581554 1.424 0.15447
## X3P NA NA NA NA
## X3PA 0.0034918 0.0040019 0.873 0.38292
## X2PA -0.0003084 0.0018774 -0.164 0.86952
## FGA NA NA NA NA
## ORB -0.0015066 0.0024775 -0.608 0.54310
## SeasonEnd -0.0496359 0.0464344 -1.069 0.28509
## TOV -0.0013896 0.0024207 -0.574 0.56593
## oppPTS -0.0003193 0.0019623 -0.163 0.87073
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.77 on 834 degrees of freedom
## Residual deviance: 269.42 on 818 degrees of freedom
## AIC: 303.42
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9110048
## 3 0.2 0.9262537
## 4 0.3 0.9321357
## 5 0.4 0.9349593
## 6 0.5 0.9388601
## 7 0.6 0.9390756
## 8 0.7 0.9277238
## 9 0.8 0.9133333
## 10 0.9 0.8780488
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.All.X.glm.N
## 1 N 330
## 2 Y 33
## Playoffs.fctr.predict.All.X.glm.Y
## 1 25
## 2 447
## Prediction
## Reference N Y
## N 330 25
## Y 33 447
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.305389e-01 8.583090e-01 9.111278e-01 9.468362e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 7.620361e-119 3.580197e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.8965517
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.9285714
## 9 0.8 0.8461538
## 10 0.9 0.8333333
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.All.X.glm.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.All.X.glm.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.006 0.073
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9839613 0.6 0.9390756 0.9125904
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9111278 0.9468362 0.8210586 0.9795918
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7649652 0.9912295 0.8571429 303.4227
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01686 0.03578204
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 53.073 57.25 4.177
## 3 fit.models_1_bayesglm 3 0 57.250 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
## Loading required package: Rcpp
##
## arm (Version 1.8-5, built: 2015-05-13)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Recitations/Unit2_NBA
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.42502 -0.06836 0.00910 0.15041 2.99093
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.418e+01 8.079e+01 0.547 0.58448
## W 4.628e-01 6.018e-02 7.690 1.47e-14 ***
## DRB 1.167e-03 1.962e-03 0.595 0.55189
## AST 3.570e-03 1.368e-03 2.609 0.00909 **
## PTS -1.889e-04 1.529e-03 -0.124 0.90168
## FT 5.514e-03 2.843e-03 1.939 0.05246 .
## FG -1.012e-03 3.189e-03 -0.317 0.75106
## BLK 1.279e-03 2.004e-03 0.638 0.52334
## FTA -1.931e-03 2.075e-03 -0.930 0.35221
## STL 5.307e-03 2.604e-03 2.038 0.04154 *
## X2P -4.399e-05 2.194e-03 -0.020 0.98400
## .rnorm 2.105e-01 1.528e-01 1.378 0.16833
## X3P -1.773e-03 4.073e-03 -0.435 0.66332
## X3PA 3.808e-04 1.555e-03 0.245 0.80657
## X2PA -1.115e-04 1.125e-03 -0.099 0.92105
## FGA 1.793e-04 1.574e-03 0.114 0.90931
## ORB -1.764e-03 2.028e-03 -0.870 0.38429
## SeasonEnd -3.328e-02 3.979e-02 -0.836 0.40290
## TOV -1.376e-03 2.024e-03 -0.680 0.49672
## oppPTS -7.295e-04 1.598e-03 -0.456 0.64809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.77 on 834 degrees of freedom
## Residual deviance: 271.01 on 815 degrees of freedom
## AIC: 311.01
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9110048
## 3 0.2 0.9263984
## 4 0.3 0.9314796
## 5 0.4 0.9361702
## 6 0.5 0.9399586
## 7 0.6 0.9390756
## 8 0.7 0.9245690
## 9 0.8 0.9072626
## 10 0.9 0.8711944
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.All.X.bayesglm.N
## 1 N 323
## 2 Y 26
## Playoffs.fctr.predict.All.X.bayesglm.Y
## 1 32
## 2 454
## Prediction
## Reference N Y
## N 323 32
## Y 26 454
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.305389e-01 8.575798e-01 9.111278e-01 9.468362e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 7.620361e-119 5.114818e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.9333333
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.9285714
## 9 0.8 0.9285714
## 10 0.9 0.8333333
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.All.X.bayesglm.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.All.X.bayesglm.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.797 0.117
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9836092 0.5 0.9399586 0.9173779
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9111278 0.9468362 0.8306003 0.9795918
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7649652 0.9912295 0.8571429 311.0097
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01859518 0.03915204
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 57.250 61.638 4.388
## 4 fit.models_1_rpart 4 0 61.639 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00563 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.811267606 0 1.0000000
## 2 0.005633803 1 0.1887324
##
## Variable importance
## W DRB AST oppPTS PTS BLK
## 66 11 7 6 5 4
##
## Node number 1: 835 observations, complexity param=0.8112676
## predicted class=Y expected loss=0.4251497 P(node) =1
## class counts: 355 480
## probabilities: 0.425 0.575
## left son=2 (328 obs) right son=3 (507 obs)
## Primary splits:
## W < 38.5 to the left, improve=285.29670, (0 missing)
## DRB < 2386 to the left, improve= 34.12326, (0 missing)
## AST < 1991.5 to the left, improve= 33.29244, (0 missing)
## oppPTS < 7961 to the right, improve= 29.65890, (0 missing)
## PTS < 8686 to the left, improve= 20.67583, (0 missing)
## Surrogate splits:
## DRB < 2372.5 to the left, agree=0.674, adj=0.171, (0 split)
## AST < 1749.5 to the left, agree=0.649, adj=0.107, (0 split)
## oppPTS < 8820 to the right, agree=0.641, adj=0.085, (0 split)
## PTS < 7726.5 to the left, agree=0.637, adj=0.076, (0 split)
## BLK < 310.5 to the left, agree=0.634, adj=0.067, (0 split)
##
## Node number 2: 328 observations
## predicted class=N expected loss=0.06097561 P(node) =0.3928144
## class counts: 308 20
## probabilities: 0.939 0.061
##
## Node number 3: 507 observations
## predicted class=Y expected loss=0.09270217 P(node) =0.6071856
## class counts: 47 460
## probabilities: 0.093 0.907
##
## n= 835
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 835 355 Y (0.42514970 0.57485030)
## 2) W< 38.5 328 20 N (0.93902439 0.06097561) *
## 3) W>=38.5 507 47 Y (0.09270217 0.90729783) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9321175
## 3 0.2 0.9321175
## 4 0.3 0.9321175
## 5 0.4 0.9321175
## 6 0.5 0.9321175
## 7 0.6 0.9321175
## 8 0.7 0.9321175
## 9 0.8 0.9321175
## 10 0.9 0.9321175
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 308
## 2 Y 20
## Playoffs.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 47
## 2 460
## Prediction
## Reference N Y
## N 308 47
## Y 20 460
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.197605e-01 8.342002e-01 8.992163e-01 9.372770e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 3.265448e-110 1.491123e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.8965517
## 3 0.2 0.8965517
## 4 0.3 0.8965517
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.8965517
## 9 0.8 0.8965517
## 10 0.9 0.8965517
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 12
## 2 Y 1
## Playoffs.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 2
## 2 13
## Prediction
## Reference N Y
## N 12 2
## Y 1 13
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.928571e-01 7.857143e-01 7.177356e-01 9.773349e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.372024e-05 1.000000e+00
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.09 0.06
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9129695 0.9 0.9321175 0.8981976
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8992163 0.937277 0.7908403 0.8928571
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.8965517 0.8928571
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7177356 0.9773349 0.7857143
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.00840949 0.01827986
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 61.639 66.101 4.462
## 5 fit.models_1_rf 5 0 66.102 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 10 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 835 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1670 matrix numeric
## oob.times 835 -none- numeric
## classes 2 -none- character
## importance 18 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 835 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 18 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9356725
## 3 0.2 0.9628887
## 4 0.3 0.9886715
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 0.9989572
## 8 0.7 0.9915966
## 9 0.8 0.9765458
## 10 0.9 0.9309577
## 11 1.0 0.2545455
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 355
## 2 Y NA
## Playoffs.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 480
## Prediction
## Reference N Y
## N 355 0
## Y 0 480
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.955919e-01 1.000000e+00 5.748503e-01
## AccuracyPValue McnemarPValue
## 1.691322e-201 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9032258
## 3 0.2 0.9333333
## 4 0.3 0.9333333
## 5 0.4 0.8965517
## 6 0.5 0.8965517
## 7 0.6 0.9285714
## 8 0.7 0.8888889
## 9 0.8 0.8000000
## 10 0.9 0.7272727
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 12
## 2 Y NA
## Playoffs.fctr.predict.All.X.no.rnorm.rf.Y
## 1 2
## 2 14
## Prediction
## Reference N Y
## N 12 2
## Y 0 14
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.285714e-01 8.571429e-01 7.649652e-01 9.912295e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.516193e-06 4.795001e-01
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 7.341 1.381
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.9197717
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9955919 1 0.8343263 0.9642857
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.9333333 0.9285714
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7649652 0.9912295 0.8571429
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0197341 0.04207761# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitobs_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
model_id <- "W.only"; indep_vars_vctr <- c(NULL
,"W", ".rnorm"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
)
for (method in c("glm")) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: W.only.glm"
## [1] " indep_vars: W, .rnorm"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.85595 -0.08894 0.01313 0.19013 2.93072
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.5144 1.6261 -11.385 <2e-16 ***
## W 0.4727 0.0407 11.614 <2e-16 ***
## .rnorm 0.1124 0.1451 0.775 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.8 on 834 degrees of freedom
## Residual deviance: 308.3 on 832 degrees of freedom
## AIC: 314.3
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9035917
## 3 0.2 0.9199219
## 4 0.3 0.9321357
## 5 0.4 0.9333333
## 6 0.5 0.9320988
## 7 0.6 0.9275971
## 8 0.7 0.9161290
## 9 0.8 0.8951522
## 10 0.9 0.8524203
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.W.only.glm.N
## 1 N 307
## 2 Y 18
## Playoffs.fctr.predict.W.only.glm.Y
## 1 48
## 2 462
## Prediction
## Reference N Y
## N 307 48
## Y 18 462
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.209581e-01 8.364931e-01 9.005347e-01 9.383443e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 3.838559e-111 3.574541e-04
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6666667
## 2 0.1 0.9333333
## 3 0.2 0.9333333
## 4 0.3 0.9333333
## 5 0.4 0.9333333
## 6 0.5 0.8965517
## 7 0.6 0.8965517
## 8 0.7 0.8965517
## 9 0.8 0.9285714
## 10 0.9 0.9629630
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## Playoffs.fctr Playoffs.fctr.predict.W.only.glm.N
## 1 N 14
## 2 Y 1
## Playoffs.fctr.predict.W.only.glm.Y
## 1 NA
## 2 13
## Prediction
## Reference N Y
## N 14 0
## Y 1 13
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.642857e-01 9.285714e-01 8.165224e-01 9.990962e-01 5.000000e-01
## AccuracyPValue McnemarPValue
## 1.080334e-07 1.000000e+00
## model_id model_method feats max.nTuningRuns
## 1 W.only.glm glm W, .rnorm 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.21 0.02 0.978257
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9333333 0.9149798
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9005347 0.9383443 0.8248854 0.9897959
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.962963 0.9642857
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8165224 0.9990962 0.9285714 314.2956
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03489742 0.07191793
## importance
## W 100
## .rnorm 0# model_id <- "W.only.no.Playoffs.fctr"; indep_vars_vctr <- c(NULL
# ,"W"
# # ,"<feat1>*<feat2>"
# # ,"<feat1>:<feat2>"
# )
# for (method in c("lm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var="Playoffs", rsp_var_out="Playoffs.predict.",
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## W.only.glm W.only.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart W, DRB
## Max.cor.Y.cv.0.cp.0.rpart W, DRB
## Max.cor.Y.rpart W, DRB
## Max.cor.Y.glm W, DRB
## Interact.High.cor.Y.glm W, DRB, W:AST, W:PTS, W:FT, W:FG, W:TOV
## Low.cor.X.glm W, DRB, AST, FT, BLK, STL, .rnorm, X3P, X3PA, X2PA, FGA, ORB, TOV
## All.X.glm W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.bayesglm W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.no.rnorm.rpart W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.no.rnorm.rf W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## W.only.glm W, .rnorm
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.305
## Random.myrandom_classfr 0 0.229
## Max.cor.Y.cv.0.rpart 0 0.635
## Max.cor.Y.cv.0.cp.0.rpart 0 0.459
## Max.cor.Y.rpart 3 1.004
## Max.cor.Y.glm 1 0.914
## Interact.High.cor.Y.glm 1 1.179
## Low.cor.X.glm 1 1.008
## All.X.glm 1 1.006
## All.X.bayesglm 1 1.797
## All.X.no.rnorm.rpart 3 1.090
## All.X.no.rnorm.rf 3 7.341
## W.only.glm 1 1.210
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.002 0.5000000
## Random.myrandom_classfr 0.002 0.4623826
## Max.cor.Y.cv.0.rpart 0.019 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.016 0.9727201
## Max.cor.Y.rpart 0.019 0.9129695
## Max.cor.Y.glm 0.022 0.9778638
## Interact.High.cor.Y.glm 0.033 0.9818075
## Low.cor.X.glm 0.053 0.9827054
## All.X.glm 0.073 0.9839613
## All.X.bayesglm 0.117 0.9836092
## All.X.no.rnorm.rpart 0.060 0.9129695
## All.X.no.rnorm.rf 1.381 1.0000000
## W.only.glm 0.020 0.9782570
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.7300380
## Max.cor.Y.cv.0.rpart 0.5 0.7300380
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.9416581
## Max.cor.Y.rpart 0.9 0.9321175
## Max.cor.Y.glm 0.3 0.9321357
## Interact.High.cor.Y.glm 0.5 0.9396111
## Low.cor.X.glm 0.6 0.9412998
## All.X.glm 0.6 0.9390756
## All.X.bayesglm 0.5 0.9399586
## All.X.no.rnorm.rpart 0.9 0.9321175
## All.X.no.rnorm.rf 0.5 1.0000000
## W.only.glm 0.4 0.9333333
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.4251497 0.3913345
## Random.myrandom_classfr 0.5748503 0.5405049
## Max.cor.Y.cv.0.rpart 0.5748503 0.5405049
## Max.cor.Y.cv.0.cp.0.rpart 0.9317365 0.9124581
## Max.cor.Y.rpart 0.9053875 0.8992163
## Max.cor.Y.glm 0.9173779 0.8978991
## Interact.High.cor.Y.glm 0.9221741 0.9097989
## Low.cor.X.glm 0.9233602 0.9137899
## All.X.glm 0.9125904 0.9111278
## All.X.bayesglm 0.9173779 0.9111278
## All.X.no.rnorm.rpart 0.8981976 0.8992163
## All.X.no.rnorm.rf 0.9197717 0.9955919
## W.only.glm 0.9149798 0.9005347
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.4594951 0.0000000 0.5000000
## Random.myrandom_classfr 0.6086655 0.0000000 0.4285714
## Max.cor.Y.cv.0.rpart 0.6086655 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.9478914 0.8594670 0.9566327
## Max.cor.Y.rpart 0.9372770 0.8048875 0.8928571
## Max.cor.Y.glm 0.9362084 0.8299249 0.9897959
## Interact.High.cor.Y.glm 0.9457796 0.8400543 0.9846939
## Low.cor.X.glm 0.9489450 0.8426415 0.9846939
## All.X.glm 0.9468362 0.8210586 0.9795918
## All.X.bayesglm 0.9468362 0.8306003 0.9795918
## All.X.no.rnorm.rpart 0.9372770 0.7908403 0.8928571
## All.X.no.rnorm.rf 1.0000000 0.8343263 0.9642857
## W.only.glm 0.9383443 0.8248854 0.9897959
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6666667
## Max.cor.Y.cv.0.rpart 0.5 0.6666667
## Max.cor.Y.cv.0.cp.0.rpart 0.9 0.9285714
## Max.cor.Y.rpart 0.9 0.8965517
## Max.cor.Y.glm 0.3 0.9333333
## Interact.High.cor.Y.glm 0.1 0.9333333
## Low.cor.X.glm 0.2 0.9333333
## All.X.glm 0.1 0.9333333
## All.X.bayesglm 0.2 0.9333333
## All.X.no.rnorm.rpart 0.9 0.8965517
## All.X.no.rnorm.rf 0.3 0.9333333
## W.only.glm 0.9 0.9629630
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.5000000 0.3064710
## Random.myrandom_classfr 0.5000000 0.3064710
## Max.cor.Y.cv.0.rpart 0.5000000 0.3064710
## Max.cor.Y.cv.0.cp.0.rpart 0.9285714 0.7649652
## Max.cor.Y.rpart 0.8928571 0.7177356
## Max.cor.Y.glm 0.9285714 0.7649652
## Interact.High.cor.Y.glm 0.9285714 0.7649652
## Low.cor.X.glm 0.9285714 0.7649652
## All.X.glm 0.9285714 0.7649652
## All.X.bayesglm 0.9285714 0.7649652
## All.X.no.rnorm.rpart 0.8928571 0.7177356
## All.X.no.rnorm.rf 0.9285714 0.7649652
## W.only.glm 0.9642857 0.8165224
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.6935290 0.0000000
## Random.myrandom_classfr 0.6935290 0.0000000
## Max.cor.Y.cv.0.rpart 0.6935290 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.9912295 0.8571429
## Max.cor.Y.rpart 0.9773349 0.7857143
## Max.cor.Y.glm 0.9912295 0.8571429
## Interact.High.cor.Y.glm 0.9912295 0.8571429
## Low.cor.X.glm 0.9912295 0.8571429
## All.X.glm 0.9912295 0.8571429
## All.X.bayesglm 0.9912295 0.8571429
## All.X.no.rnorm.rpart 0.9773349 0.7857143
## All.X.no.rnorm.rf 0.9912295 0.8571429
## W.only.glm 0.9990962 0.9285714
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.03060035 0.06433844 NA
## Max.cor.Y.glm 0.02848851 0.05891038 314.6768
## Interact.High.cor.Y.glm 0.02095379 0.04392501 297.8003
## Low.cor.X.glm 0.02391429 0.04992758 301.2685
## All.X.glm 0.01686000 0.03578204 303.4227
## All.X.bayesglm 0.01859518 0.03915204 311.0097
## All.X.no.rnorm.rpart 0.00840949 0.01827986 NA
## All.X.no.rnorm.rf 0.01973410 0.04207761 NA
## W.only.glm 0.03489742 0.07191793 314.2956rm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 66.102 80.874 14.772
## 6 fit.models_1_end 6 0 80.875 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 49.400 80.887 31.487
## 12 fit.models 7 2 80.887 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## W.only.glm W.only.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart W, DRB
## Max.cor.Y.cv.0.cp.0.rpart W, DRB
## Max.cor.Y.rpart W, DRB
## Max.cor.Y.glm W, DRB
## Interact.High.cor.Y.glm W, DRB, W:AST, W:PTS, W:FT, W:FG, W:TOV
## Low.cor.X.glm W, DRB, AST, FT, BLK, STL, .rnorm, X3P, X3PA, X2PA, FGA, ORB, TOV
## All.X.glm W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.bayesglm W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, .rnorm, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.no.rnorm.rpart W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## All.X.no.rnorm.rf W, DRB, AST, PTS, FT, FG, BLK, FTA, STL, X2P, X3P, X3PA, X2PA, FGA, ORB, SeasonEnd, TOV, oppPTS
## W.only.glm W, .rnorm
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.4623826
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.9727201
## Max.cor.Y.rpart 3 0.9129695
## Max.cor.Y.glm 1 0.9778638
## Interact.High.cor.Y.glm 1 0.9818075
## Low.cor.X.glm 1 0.9827054
## All.X.glm 1 0.9839613
## All.X.bayesglm 1 0.9836092
## All.X.no.rnorm.rpart 3 0.9129695
## All.X.no.rnorm.rf 3 1.0000000
## W.only.glm 1 0.9782570
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.7300380
## Max.cor.Y.cv.0.rpart 0.5 0.7300380
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.9416581
## Max.cor.Y.rpart 0.9 0.9321175
## Max.cor.Y.glm 0.3 0.9321357
## Interact.High.cor.Y.glm 0.5 0.9396111
## Low.cor.X.glm 0.6 0.9412998
## All.X.glm 0.6 0.9390756
## All.X.bayesglm 0.5 0.9399586
## All.X.no.rnorm.rpart 0.9 0.9321175
## All.X.no.rnorm.rf 0.5 1.0000000
## W.only.glm 0.4 0.9333333
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.4251497 0.0000000 0.5000000
## Random.myrandom_classfr 0.5748503 0.0000000 0.4285714
## Max.cor.Y.cv.0.rpart 0.5748503 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.9317365 0.8594670 0.9566327
## Max.cor.Y.rpart 0.9053875 0.8048875 0.8928571
## Max.cor.Y.glm 0.9173779 0.8299249 0.9897959
## Interact.High.cor.Y.glm 0.9221741 0.8400543 0.9846939
## Low.cor.X.glm 0.9233602 0.8426415 0.9846939
## All.X.glm 0.9125904 0.8210586 0.9795918
## All.X.bayesglm 0.9173779 0.8306003 0.9795918
## All.X.no.rnorm.rpart 0.8981976 0.7908403 0.8928571
## All.X.no.rnorm.rf 0.9197717 0.8343263 0.9642857
## W.only.glm 0.9149798 0.8248854 0.9897959
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6666667
## Max.cor.Y.cv.0.rpart 0.5 0.6666667
## Max.cor.Y.cv.0.cp.0.rpart 0.9 0.9285714
## Max.cor.Y.rpart 0.9 0.8965517
## Max.cor.Y.glm 0.3 0.9333333
## Interact.High.cor.Y.glm 0.1 0.9333333
## Low.cor.X.glm 0.2 0.9333333
## All.X.glm 0.1 0.9333333
## All.X.bayesglm 0.2 0.9333333
## All.X.no.rnorm.rpart 0.9 0.8965517
## All.X.no.rnorm.rf 0.3 0.9333333
## W.only.glm 0.9 0.9629630
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5000000 0.0000000
## Random.myrandom_classfr 0.5000000 0.0000000
## Max.cor.Y.cv.0.rpart 0.5000000 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.9285714 0.8571429
## Max.cor.Y.rpart 0.8928571 0.7857143
## Max.cor.Y.glm 0.9285714 0.8571429
## Interact.High.cor.Y.glm 0.9285714 0.8571429
## Low.cor.X.glm 0.9285714 0.8571429
## All.X.glm 0.9285714 0.8571429
## All.X.bayesglm 0.9285714 0.8571429
## All.X.no.rnorm.rpart 0.8928571 0.7857143
## All.X.no.rnorm.rf 0.9285714 0.8571429
## W.only.glm 0.9642857 0.9285714
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 3.2786885 500.000000
## Random.myrandom_classfr 4.3668122 500.000000
## Max.cor.Y.cv.0.rpart 1.5748031 52.631579
## Max.cor.Y.cv.0.cp.0.rpart 2.1786492 62.500000
## Max.cor.Y.rpart 0.9960159 52.631579
## Max.cor.Y.glm 1.0940919 45.454545
## Interact.High.cor.Y.glm 0.8481764 30.303030
## Low.cor.X.glm 0.9920635 18.867925
## All.X.glm 0.9940358 13.698630
## All.X.bayesglm 0.5564830 8.547009
## All.X.no.rnorm.rpart 0.9174312 16.666667
## All.X.no.rnorm.rf 0.1362212 0.724113
## W.only.glm 0.8264463 50.000000
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.003177864
## Interact.High.cor.Y.glm 0.003357955
## Low.cor.X.glm 0.003319298
## All.X.glm 0.003295733
## All.X.bayesglm 0.003215333
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NA
## W.only.glm 0.003181718print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 4 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 102 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
#stop(here")
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 13 W.only.glm 0.9642857 0.9897959 0.9285714
## 6 Max.cor.Y.glm 0.9285714 0.9897959 0.8571429
## 7 Interact.High.cor.Y.glm 0.9285714 0.9846939 0.8571429
## 8 Low.cor.X.glm 0.9285714 0.9846939 0.8571429
## 9 All.X.glm 0.9285714 0.9795918 0.8571429
## 10 All.X.bayesglm 0.9285714 0.9795918 0.8571429
## 12 All.X.no.rnorm.rf 0.9285714 0.9642857 0.8571429
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.9285714 0.9566327 0.8571429
## 5 Max.cor.Y.rpart 0.8928571 0.8928571 0.7857143
## 11 All.X.no.rnorm.rpart 0.8928571 0.8928571 0.7857143
## 1 MFO.myMFO_classfr 0.5000000 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5000000 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.5000000 0.4285714 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 13 314.2956 0.9
## 6 314.6768 0.3
## 7 297.8003 0.1
## 8 301.2685 0.2
## 9 303.4227 0.1
## 10 311.0097 0.2
## 12 NA 0.3
## 4 NA 0.9
## 5 NA 0.9
## 11 NA 0.9
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 44 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: W.only.glm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.85595 -0.08894 0.01313 0.19013 2.93072
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.5144 1.6261 -11.385 <2e-16 ***
## W 0.4727 0.0407 11.614 <2e-16 ***
## .rnorm 0.1124 0.1451 0.775 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.8 on 834 degrees of freedom
## Residual deviance: 308.3 on 832 degrees of freedom
## AIC: 314.3
##
## Number of Fisher Scoring iterations: 8## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); #sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance W.only.glm.importance
## W 100 100
## .rnorm 0 0# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.W.only.glm.prob
## 850 15 Y 0.4115584899
## 849 14 Y 0.9999969053
## 855 20 N 0.0001227762
## 861 26 N 0.0652014350
## Playoffs.fctr.predict.W.only.glm
## 850 N
## 849 Y
## 855 N
## 861 N
## Playoffs.fctr.predict.W.only.glm.accurate
## 850 FALSE
## 849 TRUE
## 855 TRUE
## 861 TRUE
## Playoffs.fctr.predict.W.only.glm.error .label
## 850 -0.4884415 15
## 849 0.0000000 14
## 855 0.0000000 20
## 861 0.0000000 26
## [1] "Inaccurate: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.W.only.glm.prob
## 850 15 Y 0.4115585
## Playoffs.fctr.predict.W.only.glm
## 850 N
## Playoffs.fctr.predict.W.only.glm.accurate
## 850 FALSE
## Playoffs.fctr.predict.W.only.glm.error
## 850 -0.4884415
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 80.887 94.811 13.924
## 13 fit.models 7 3 94.811 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "Playoffs.fctr.predict.W.only.glm.prob"
## [2] "Playoffs.fctr.predict.W.only.glm"
## [3] "Playoffs.fctr.predict.W.only.glm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 94.811 98.352 3.541
## 14 fit.data.training 8 0 98.352 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.glm"
## [1] " indep_vars: W, .rnorm"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.85595 -0.08894 0.01313 0.19013 2.93072
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.5144 1.6261 -11.385 <2e-16 ***
## W 0.4727 0.0407 11.614 <2e-16 ***
## .rnorm 0.1124 0.1451 0.775 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1138.8 on 834 degrees of freedom
## Residual deviance: 308.3 on 832 degrees of freedom
## AIC: 314.3
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.7300380
## 2 0.1 0.9035917
## 3 0.2 0.9199219
## 4 0.3 0.9321357
## 5 0.4 0.9333333
## 6 0.5 0.9320988
## 7 0.6 0.9275971
## 8 0.7 0.9161290
## 9 0.8 0.8951522
## 10 0.9 0.8524203
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Playoffs.fctr Playoffs.fctr.predict.Final.glm.N
## 1 N 307
## 2 Y 18
## Playoffs.fctr.predict.Final.glm.Y
## 1 48
## 2 462
## Prediction
## Reference N Y
## N 307 48
## Y 18 462
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.209581e-01 8.364931e-01 9.005347e-01 9.383443e-01 5.748503e-01
## AccuracyPValue McnemarPValue
## 3.838559e-111 3.574541e-04## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method feats max.nTuningRuns
## 1 Final.glm glm W, .rnorm 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.014 0.021 0.978257
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9333333 0.9149798
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.9005347 0.9383443 0.8248854 314.2956
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03489742 0.07191793rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 98.352 104.118 5.767
## 15 fit.data.training 8 1 104.119 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.9sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## W.only.glm.importance importance Final.glm.importance
## W 100 100 100
## .rnorm 0 0 0if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 201 201 Y 9.989186e-01
## 318 318 N 1.788424e-06
## 397 397 Y 9.999998e-01
## 564 564 Y 9.158155e-01
## Playoffs.fctr.predict.Final.glm
## 201 Y
## 318 N
## 397 Y
## 564 Y
## Playoffs.fctr.predict.Final.glm.accurate
## 201 TRUE
## 318 TRUE
## 397 TRUE
## 564 TRUE
## Playoffs.fctr.predict.Final.glm.error .label
## 201 0 201
## 318 0 318
## 397 0 397
## 564 0 564
## [1] "Inaccurate: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 140 140 Y 0.01364261
## 203 203 Y 0.01672342
## 368 368 Y 0.11007627
## 114 114 Y 0.11388258
## 157 157 Y 0.12520065
## 118 118 Y 0.16587698
## Playoffs.fctr.predict.Final.glm
## 140 N
## 203 N
## 368 N
## 114 N
## 157 N
## 118 N
## Playoffs.fctr.predict.Final.glm.accurate
## 140 FALSE
## 203 FALSE
## 368 FALSE
## 114 FALSE
## 157 FALSE
## 118 FALSE
## Playoffs.fctr.predict.Final.glm.error
## 140 -0.8863574
## 203 -0.8832766
## 368 -0.7899237
## 114 -0.7861174
## 157 -0.7747994
## 118 -0.7341230
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 549 549 Y 0.7611855
## 694 694 Y 0.7896040
## 187 187 Y 0.8014601
## 610 610 Y 0.8223485
## 528 528 Y 0.8686152
## 142 142 Y 0.8987021
## Playoffs.fctr.predict.Final.glm
## 549 N
## 694 N
## 187 N
## 610 N
## 528 N
## 142 N
## Playoffs.fctr.predict.Final.glm.accurate
## 549 FALSE
## 694 FALSE
## 187 FALSE
## 610 FALSE
## 528 FALSE
## 142 FALSE
## Playoffs.fctr.predict.Final.glm.error
## 549 -0.138814545
## 694 -0.110396012
## 187 -0.098539925
## 610 -0.077651519
## 528 -0.031384848
## 142 -0.001297934
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 642 642 N 0.9107223
## 519 519 N 0.9328332
## 53 53 N 0.9364528
## 79 79 N 0.9368984
## 769 769 N 0.9590288
## 724 724 N 0.9830626
## Playoffs.fctr.predict.Final.glm
## 642 Y
## 519 Y
## 53 Y
## 79 Y
## 769 Y
## 724 Y
## Playoffs.fctr.predict.Final.glm.accurate
## 642 FALSE
## 519 FALSE
## 53 FALSE
## 79 FALSE
## 769 FALSE
## 724 FALSE
## Playoffs.fctr.predict.Final.glm.error
## 642 0.01072235
## 519 0.03283320
## 53 0.03645277
## 79 0.03689837
## 769 0.05902879
## 724 0.08306264
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "Playoffs.fctr.predict.Final.glm.prob"
## [2] "Playoffs.fctr.predict.Final.glm"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 104.119 107.445 3.326
## 16 predict.data.new 9 0 107.445 NA NAStep 9.0: predict data new
# Compute final model predictions
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.9if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 850 15 Y 0.4115584899
## 849 14 Y 0.9999969053
## 855 20 N 0.0001227762
## 861 26 N 0.0652014350
## Playoffs.fctr.predict.Final.glm
## 850 N
## 849 Y
## 855 N
## 861 N
## Playoffs.fctr.predict.Final.glm.accurate
## 850 FALSE
## 849 TRUE
## 855 TRUE
## 861 TRUE
## Playoffs.fctr.predict.Final.glm.error .label
## 850 -0.4884415 15
## 849 0.0000000 14
## 855 0.0000000 20
## 861 0.0000000 26
## [1] "Inaccurate: "
## .rownames Playoffs.fctr Playoffs.fctr.predict.Final.glm.prob
## 850 15 Y 0.4115585
## Playoffs.fctr.predict.Final.glm
## 850 N
## Playoffs.fctr.predict.Final.glm.accurate
## 850 FALSE
## Playoffs.fctr.predict.Final.glm.error
## 850 -0.4884415
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.9print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: W.only.glm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glm"print(dim(glb_fitobs_df))## [1] 835 25print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 13 W.only.glm 0.9642857 0.9897959 0.9285714
## 6 Max.cor.Y.glm 0.9285714 0.9897959 0.8571429
## 7 Interact.High.cor.Y.glm 0.9285714 0.9846939 0.8571429
## 8 Low.cor.X.glm 0.9285714 0.9846939 0.8571429
## 9 All.X.glm 0.9285714 0.9795918 0.8571429
## 10 All.X.bayesglm 0.9285714 0.9795918 0.8571429
## 12 All.X.no.rnorm.rf 0.9285714 0.9642857 0.8571429
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.9285714 0.9566327 0.8571429
## 5 Max.cor.Y.rpart 0.8928571 0.8928571 0.7857143
## 11 All.X.no.rnorm.rpart 0.8928571 0.8928571 0.7857143
## 1 MFO.myMFO_classfr 0.5000000 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5000000 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.5000000 0.4285714 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 13 314.2956 0.9
## 6 314.6768 0.3
## 7 297.8003 0.1
## 8 301.2685 0.2
## 9 303.4227 0.1
## 10 311.0097 0.2
## 12 NA 0.3
## 4 NA 0.9
## 5 NA 0.9
## 11 NA 0.9
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "W.only.glm OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 14 0
## Y 1 13
## [1] "Final.glm new confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 14 0
## Y 1 13dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## W.only.glm.importance importance Final.glm.importance
## W 100 100 100
## .rnorm 0 0 0# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 107.445 109.703 2.258
## 17 display.session.info 10 0 109.704 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 49.400 80.887 31.487
## 10 fit.models 7 0 26.080 49.399 23.319
## 12 fit.models 7 2 80.887 94.811 13.924
## 2 inspect.data 2 0 10.803 20.805 10.002
## 14 fit.data.training 8 0 98.352 104.118 5.767
## 13 fit.models 7 3 94.811 98.352 3.541
## 15 fit.data.training 8 1 104.119 107.445 3.326
## 3 scrub.data 2 1 20.805 23.074 2.269
## 16 predict.data.new 9 0 107.445 109.703 2.258
## 6 extract.features 3 0 23.202 24.669 1.467
## 8 select.features 5 0 24.983 25.736 0.754
## 1 import.data 1 0 10.377 10.802 0.425
## 9 partition.data.training 6 0 25.737 26.080 0.343
## 7 cluster.data 4 0 24.669 24.983 0.314
## 5 manage.missing.data 2 3 23.109 23.202 0.093
## 4 transform.data 2 2 23.074 23.109 0.035
## duration
## 11 31.487
## 10 23.319
## 12 13.924
## 2 10.002
## 14 5.766
## 13 3.541
## 15 3.326
## 3 2.269
## 16 2.258
## 6 1.467
## 8 0.753
## 1 0.425
## 9 0.343
## 7 0.314
## 5 0.093
## 4 0.035
## [1] "Total Elapsed Time: 109.703 secs"
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 66.102 80.874 14.772
## 4 fit.models_1_rpart 4 0 61.639 66.101 4.462
## 3 fit.models_1_bayesglm 3 0 57.250 61.638 4.388
## 2 fit.models_1_glm 2 0 53.073 57.250 4.177
## 1 fit.models_1_bgn 1 0 53.058 53.072 0.014
## duration
## 5 14.772
## 4 4.462
## 3 4.388
## 2 4.177
## 1 0.014
## [1] "Total Elapsed Time: 80.874 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.16.1 randomForest_4.6-10 arm_1.8-5
## [4] lme4_1.1-7 Rcpp_0.11.6 Matrix_1.2-1
## [7] MASS_7.3-40 rpart.plot_1.5.2 rpart_4.1-9
## [10] ROCR_1.0-7 gplots_2.17.0 dplyr_0.4.1
## [13] plyr_1.8.2 sqldf_0.4-10 RSQLite_1.0.0
## [16] DBI_0.3.1 gsubfn_0.6-6 proto_0.3-10
## [19] reshape2_1.4.1 doMC_1.3.3 iterators_1.0.7
## [22] foreach_1.4.2 doBy_4.5-13 survival_2.38-1
## [25] caret_6.0-47 ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] class_7.3-12 gtools_3.5.0 assertthat_0.1
## [4] digest_0.6.8 BradleyTerry2_1.0-6 chron_2.3-45
## [7] evaluate_0.7 coda_0.17-1 e1071_1.6-4
## [10] lazyeval_0.1.10 minqa_1.2.4 SparseM_1.6
## [13] car_2.0-25 nloptr_1.0.4 rmarkdown_0.6.1
## [16] labeling_0.3 splines_3.2.0 stringr_1.0.0
## [19] munsell_0.4.2 compiler_3.2.0 mgcv_1.8-6
## [22] htmltools_0.2.6 nnet_7.3-9 codetools_0.2-11
## [25] brglm_0.5-9 bitops_1.0-6 nlme_3.1-120
## [28] gtable_0.1.2 magrittr_1.5 formatR_1.2
## [31] scales_0.2.4 KernSmooth_2.23-14 stringi_0.4-1
## [34] RColorBrewer_1.1-2 tools_3.2.0 abind_1.4-3
## [37] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [40] caTools_1.17.1 knitr_1.10.5 quantreg_5.11